Staff AI Engineer
Job Description
About Workato
Workato is the leading Control and Execution Platform for Enterprise AI — the neutral platform enterprises trust to put AI to work across their business. Workato unifies data, applications, and processes into a single platform so AI can reliably orchestrate business processes in production at enterprise scale. Built on more than a decade of running mission-critical processes for over half the Fortune 500 — including Nasdaq, Amazon, Cisco, Vodafone, Atlassian, and Lucid Motors — Workato turns over 14,000 enterprise systems AI needs to act on into one governed execution layer. For more information, visit workato.com.
Why join us?
Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.
But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.
If this sounds right up your alley, please submit an application. We look forward to getting to know you!
Also, feel free to check out why:
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Business Insider named us an “enterprise startup to bet your career on”
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Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
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Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
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Quartz ranked us the #1 best company for remote workers
Responsibilities
As we work towards building out the Context Layer for the Agentic Enterprise, we are looking for an exceptional Search/AI Engineer with experience in Search Relevance to join our growing team. In this role, you will lead the design, development, and optimization of intelligent search systems that leverage machine learning at their core. You’ll be responsible for building end-to-end retrieval pipelines that incorporate advanced techniques in query understanding, ranking, and entity recognition. The ideal candidate combines deep expertise in information retrieval and search relevance with hands-on experience applying machine learning to real-world search problems at scale.
In this role, you will also be responsible for:
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Lead the development of advanced query understanding systems that parse natural language, resolve ambiguity, and infer user intent
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Design and deploy learning-to-rank models that optimize relevance using behavioral signals, embeddings, and structured feedback
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Build and scale robust Entity Recognition pipelines that enhance document understanding, enable contextual disambiguation, and support entity-aware retrieval
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Architect next-gen search infrastructure capable of supporting highly dynamic document corpora and real-time indexing
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Create and maintain graph-based knowledge systems that enhance LLM capabilities through structured relationship data
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Drive improvements in query rewriting, intent classification, and semantic search, using both statistical and neural methods
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Own the design of evaluation frameworks for offline/online relevance testing, A/B experimentation, and continual model tuning
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Collaborate with product and applied research teams to translate user needs into data-informed search innovations
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Produce clean, scalable code and influence system architecture and roadmap across the relevance and platform stack
Requirements
Qualifications / Experience / Technical Skills
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Bachelor's/Master's/PhD degree in Statistics, Mathematics, Computer Science, or another quantitative field
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7+ years of backend engineering experience with 3+ years in search, information retrieval, or related fields
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Strong proficiency in Python
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Hands-on experience with search engines (Opensearch or Elasticsearch)
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Strong understanding of information retrieval concepts spanning traditional methods (TF-IDF, BM25) and modern neural search techniques (vector embeddings, transformer models)
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Experience with text processing, NLP, and relevance tuning
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Experience with relevance evaluation metrics (NDCG, MRR, MAP)
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Experience with large-scale distributed systems
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Proficiency in Knowledge Graph construction and optimization is a plus
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Strong analytical and problem-solving skills
Soft Skills / Personal Characteristics
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Strong communication abilities to explain technical concepts
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Collaborative mindset for cross-functional teamwork
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Detail-oriented with strong focus on quality
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Self-motivated and able to work independently
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Passion for solving complex search problems
(REQ ID: 2472)
Requirements
Department: Product & Engineering